The automatic recognition of faces is becoming increasingly important in several applications such as security and processing of digital photographs. In security applications, automatic face recognition can be used to identify persons of interest or to confirm the identity of a person seeking access to a resource. In digital photography applications, automatic face recognition can be used to identify the people within each photograph. The knowledge of which people are in which photographs can be used to help organize the collection.
Conventional techniques for automatic face recognition do not perform satisfactorily for various reasons. Although automatic face recognition when presented with an image condition of frontal face with indoor lighting can be performed with 90% accuracy, the accuracy reduces significantly when the image conditions, such as pose, illumination, and expression, vary. For example, when a face of an image is at a 45 degree angle with an exaggerated expression (e.g., big smile) under poor illumination, it can be extremely difficult to automatically recognize the identity of the face.
Conventional techniques use various strategies to automatically recognize faces. Some techniques attempt to normalize target faces to a standard image condition. Some 2D techniques normalize target faces to an image condition that is the same as the image condition of a corpus of faces. Some 3D techniques attempt to warp non-frontal faces to frontal faces using a cylinder geometry. These techniques may train a classifier using the corpus or check specific features that are invariant to different image conditions. Because the 2D techniques do not consider specific structures of faces, their results can be less than acceptable. Although the 3D techniques overcome this limitation, they may require manual labeling or may be time-consuming.
Other techniques utilize a corpus with multiple images of a face covering different image conditions. These techniques then try to match a target face to one of the multiple images. It can be, however, very time-consuming to create the corpus or difficult to collect multiple images when the persons of interest are not readily available. Moreover, in practice, since the images of target faces may not match the image conditions of the corpus, the results of recognition can be less than acceptable.
It would be desirable to have a technique for automatic recognition of faces or other objects that would overcome some of the limitations of these conventional techniques.